Do inputs matter?: using data-dependence profiling to evaluate thread level speculation in BG/Q
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Figure 1 shows the performance of three parallel versions (auto-SIMDized, auto-SIMDized+auto-OpenMP by bgxlc r and auto-SIMDized+auto-OpenMP+speculatively parallelized by an automatic speculative parallelization framework developed) of the SPEC2006 and PolyBench/C benchmarks. The speculative loops in lbm have 98% coverage that accounts for the speedup while in bzip2(35%) and dynprog (26%), the poor coverage of speculative loops introduces overhead. h264ref has the highest number of loops speculatively parallelized (47) but most of them have function calls that introduce dependences, thus causing slowdown (only 12% of speculative threads successfully committed). Filtering speculative execution of loops with non-side-effect-free function calls tackles the mispeculation overhead. cholesky and dynprog experience L1 cache misses due to LR mode(12% and 10% respectively) while jacobi and seidel experience huge dynamic path length increase (112% and 123% respectively over sequential).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it